Detector arrays for imaging frequently have a large number of detector elements, each element having its own output. However, this straightforward architecture often proves problematic in practice, because detector arrays often have a large number of detector elements, and the correspondingly large number of outputs can increase cost and complexity. Multiplexing can reduce the number of channels—lowering the cost by reducing the number of connections or components used to implement the network. Accordingly, multiplexing detector array outputs (such that there are fewer outputs than detector array elements) is of great interest, and has been extensively investigated.
Time multiplexing can be employed, where pixel elements are read out sequentially over a single channel as opposed to being read out in parallel over numerous channels. However, this approach undesirably reduces the frame rate.
In general, sampling refers to any approach for creating a digital signal from an analog signal. For an imaging array, conventional sampling theory suggests that the number of samples be on the order of the number of pixels. However, if it is known a priori that the image is sparse in some domain, the number of samples required can be significantly reduced. Techniques that exploit this kind of sparsity are often referred to as compressed sensing (CS) approaches. Other terms that have been used for this general idea include compressed sampling, compressive sampling, compressive sensing, etc. However, CS approaches unfortunately tend to perform poorly in the presence of noise, which can significantly limit the practical application of CS methods.
Accordingly, some workers are considering modifications of the normal CS approach in an attempt to improve performance. One example is the work of Trzasko et al. (US 2011/0058719), where conventional image reconstruction (e.g., a low resolution image) is used to provide an estimate of the spatial support of the image. CS image reconstruction is then performed using this spatial support estimate as a priori information for the CS image reconstruction.
However, it remains challenging to realize the theoretical benefits of CS approaches in the presence of noise.